import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import pandas as pd

data = pd.read_csv("World_Population_Data.csv", encoding='cp1252')

Check the dataset and data types

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 234 entries, 0 to 233
Data columns (total 13 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Index                    234 non-null    int64  
 1   Country (or dependency)  234 non-null    object 
 2   Continent                234 non-null    object 
 3   Population (2023)        234 non-null    object 
 4   Yearly Change            234 non-null    object 
 5   Net Change               234 non-null    object 
 6   Density (P/Kmý)          234 non-null    object 
 7   Land Area (Kmý)          234 non-null    object 
 8   Migrants (net)           234 non-null    object 
 9   Fert.  Rate              233 non-null    float64
 10  Med. Age                 233 non-null    float64
 11  Urban Pop %              234 non-null    object 
 12  World Share              234 non-null    object 
dtypes: float64(2), int64(1), object(10)
memory usage: 23.9+ KB

data

	Index	Country (or dependency)	Continent	Population (2023)	Yearly Change	Net Change	Density (P/Kmý)	Land Area (Kmý)	Migrants (net)	Fert. Rate	Med. Age	Urban Pop %	World Share
0	1	India	Asia	1,428,627,663	0.81 %	11,454,490	481	2,973,190	-486,136	2.0	28.0	36 %	17.76 %
1	2	China	Asia	1,425,671,352	-0.02 %	-215,985	152	9,388,211	-310,220	1.2	39.0	65 %	17.72 %
2	3	United States	America	339,996,563	0.50 %	1,706,706	37	9,147,420	999,700	1.7	38.0	83 %	4.23 %
3	4	Indonesia	Asia	277,534,122	0.74 %	2,032,783	153	1,811,570	-49,997	2.1	30.0	59 %	3.45 %
4	5	Pakistan	Asia	240,485,658	1.98 %	4,660,796	312	770,880	-165,988	3.3	21.0	35 %	2.99 %
...	...	...	...	...	...	...	...	...	...	...	...	...	...
229	230	Montserrat	America	4,386	-0.09 %	-4	44	100	0	1.6	44.0	11 %	0.00 %
230	231	Falkland Islands	America	3,791	0.29 %	11	0	12,170	0	1.6	40.0	62 %	0.00 %
231	232	Niue	Oceania	1,935	0.05 %	1	7	260	0	2.4	36.0	41 %	0.00 %
232	233	Tokelau	Oceania	1,893	1.18 %	22	189	10	0	2.6	27.0	0 %	0.00 %
233	234	Holy See	Europe	518	1.57 %	8	1,295	0	0	NaN	NaN	N.A.	0.00 %

234 rows × 13 columns

Fix the population data type

data["Population (2023)"].replace(',','', regex=True, inplace=True)

data["World Share"].replace(' %', '', regex=True, inplace=True)

data["Population (2023)"] = pd.to_numeric(data["Population (2023)"])

data["World Share"] = pd.to_numeric(data["World Share"])

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 234 entries, 0 to 233
Data columns (total 13 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Index                    234 non-null    int64  
 1   Country (or dependency)  234 non-null    object 
 2   Continent                234 non-null    object 
 3   Population (2023)        234 non-null    int64  
 4   Yearly Change            234 non-null    object 
 5   Net Change               234 non-null    object 
 6   Density (P/Kmý)          234 non-null    object 
 7   Land Area (Kmý)          234 non-null    object 
 8   Migrants (net)           234 non-null    object 
 9   Fert.  Rate              233 non-null    float64
 10  Med. Age                 233 non-null    float64
 11  Urban Pop %              234 non-null    object 
 12  World Share              234 non-null    float64
dtypes: float64(3), int64(2), object(8)
memory usage: 23.9+ KB

Total population in our planet

Toral_population = data['Population (2023)'].sum()

print(f"Total Population in our planet: {Toral_population}")

Total Population in our planet: 8043901603

Total population in our planet by continent

Toral_population_by_continent = data.groupby('Continent')['Population (2023)'].sum().reset_index().sort_values(by=['Population (2023)'], ascending=False)

Toral_population_by_continent

	Continent	Population (2023)
2	Asia	4751819588
0	Africa	1460481772
1	America	1043901526
3	Europe	741693851
4	Oceania	46004866

Visualize population by country

Country = data["Country (or dependency)"]
Population = data["Population (2023)"]
Percentage = data["World Share"]
Continent = data["Continent"]

from kaleido.scopes.plotly import PlotlyScope

import plotly.io as pio
pio.renderers.default = "png"

fig = px.treemap(data_frame=data, 
                 path=[px.Constant("World Population by Country"), Country],          
                 values=Population,
                 color=Percentage,
                 color_continuous_scale=["#E4F1FF", "#0174BE", "#0174BE", "#4477CE"],
                )


fig.update_traces(
    textinfo="label+value+percent parent",
)

fig.update_layout(margin=dict(t=30, l=30, r=20, b=20),)

fig.update_coloraxes(showscale=False)

fig.show("notebook")

Visualize by Continent

fig = px.treemap(
    data_frame=data,
    path=[px.Constant("World Population by Continent"), Continent, Country],
    values=Population,
    color=Percentage,
    color_continuous_scale=["#D8E9F0", "#0174BE", "#0174BE", "#4477CE"],
    branchvalues='total',
    labels={'Percentage': 'World Share'},
)

fig.update_traces(
    textinfo="label+percent parent",
    hovertemplate=None,
    hoverinfo="skip",
)

fig.update_layout(margin=dict(t=30, l=30, r=20, b=20),
                  font=dict(
                      family="verdana",
                      size=14
                  ))


fig.update_coloraxes(showscale=False)

fig.show("notebook")

Grouping data by continent and world share percentage

I will use it to see how much each continent covers the total world population.

world_share_group = data.groupby('Continent')["World Share"].sum().reset_index()

world_share_group

	Continent	World Share
0	Africa	18.17
1	America	12.97
2	Asia	59.08
3	Europe	9.24
4	Oceania	0.55

world_share_group.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 2 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Continent    5 non-null      object 
 1   World Share  5 non-null      float64
dtypes: float64(1), object(1)
memory usage: 208.0+ bytes

Import Bubble Chart from local file

from packed_bubbles import BubbleChart
from matplotlib import rcParams

unique_continent = world_share_group["Continent"]
world_share_by_continent = world_share_group["World Share"]

Create bubble chart

color_continuous_scale=["#5DAE8B", '#71a9f7', "#1450A3", "#FFD00C", "#FF9C6D" ]


bubble_chart = BubbleChart(area=world_share_by_continent,
                           bubble_spacing=0.5)

bubble_chart.collapse()

fig, ax = plt.subplots(subplot_kw=dict(aspect="equal"),)


bubble_chart.plot(
    ax, np.ma.masked_where(world_share_by_continent <= 1, world_share_by_continent.round(2)), color_continuous_scale)


legend = plt.legend(unique_continent, loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=5, frameon=False)
for text in legend.get_texts():
    text.set_color("black")


font = {'size': 14}

 
# using rc function
plt.rc('font', **font)


COLOR = 'white'
plt.rcParams['text.color'] = COLOR

ax.axis("off")
ax.relim()
ax.autoscale_view()
ax.set_title('"World population cover by each continent"', loc='center')


plt.show()

world_share_group_sorted = world_share_group.sort_values(by="World Share", ascending=False)

fig = go.Figure(data=[go.Bar(
            x=world_share_group_sorted["Continent"], 
            y=world_share_group_sorted["World Share"].round(2),
            text=world_share_group_sorted["World Share"].round(2),
            textposition='auto',
            marker=dict(color='#4477CE')
        )])

fig.update_layout(
    plot_bgcolor='rgba(0,0,0,0)', 
    paper_bgcolor='rgba(0,0,0,0)',
    title=("World population cover by each continent (Bar chart)")
)

fig.show("notebook")